Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/330436 
Year of Publication: 
2025
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 66 [Issue:] 6 [Article No.:] 134 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
This paper proposes robust estimators of the variogram, a statistical tool that is commonly used in geostatistics to capture the spatial dependence structure of data. The new estimators are based on the highly robust minimum covariance determinant estimator and estimate the directional variogram for several lags jointly. Simulations and breakdown considerations confirm the good robustness properties of the new estimators. While Genton's estimator based on the robust estimation of the variance of pairwise sums and differences performs well in case of isolated outliers, the new estimators based on robust estimation of multivariate variance and covariance matrices perform superior to the established alternatives in the presence of outlier blocks in the data. The methods are illustrated by an application to satellite data, where outlier blocks may occur because of e.g. clouds.
Subjects: 
Breakdown point
Local changes
MCD estimator
Robustness
Spatial data
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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